Estimating multiway error-components models with correlated effects in Stata

I derive new estimators and tests of correlated effects for the (possibly) unbalanced multiway error component model (ECM), extending existing results in various aspects. The results by Kang (1985) on specification tests, who extended Hausman and Taylor (1981) and Mundlak (1978) to the two-way balanced model, emerge as particular cases of the present analysis. Davis (2002), who extends the analysis of Wansbeek and Kaptein (1989) to the multiway unbalanced model does not consider the cases of either correlated effects or specification tests. I also uncover new algebraic properties of the multiway ECM covariance matrix that prove useful for both computational and analytical purposes. Finally, I provide some examples using Stata.

Single Bunch Instabilities in FCC-ee

FCC-ee is a high luminosity lepton collider with a centre-of-mass energy from 91 to 365 GeV. Due to the machine parameters and pipe dimensions, collective effects due to electromagnetic fields produced by the interaction of the beam with the vacuum chamber can be one of the main limitations to the machine performance. In this frame, an impedance model is required to analyze these instabilities and to find possible solutions for their mitigation. This paper will present the contributions of specific machine components to the total impedance budget and their effects on the beam stability. Single bunch instability thresholds will be estimated in both transverse and longitudinal planes

New wine in new bottles: Visualizing the progression over time of the epidemics of tobacco smoking and obesity through the use of modified population pyramids

Tobacco smoking and obesity greatly contribute to premature death and disease in developed countries. In order to measure the extent to which these risk factors affect a population, as well as to describe the progression of these epidemics over time, routine surveillance of the prevalence of obesity and smoking is carried out by international organizations, national departments of health, and statistical offices. To this end, summary measures—like age-standardized rates, and tabular and graphical representations, such as maps—are used. In this study, we argue that population pyramids, a widely used demographic tool, may be easily adapted to provide relevant visual information for public health purposes. By means of two juxtaposed histograms, one for each gender, population pyramids show either the proportion or the actual number of subjects in each age and gender subgroup. We suggest that stratifying each bar of the two histograms according to ordinal categories of the health condition or risk factor examined may provide useful details on the relationship between this condition or factor and key demographic variables like age and gender. In addition, the actual number of exposed subjects can be immediately read from the graph. We therefore built a statistical routine with Stata to create modified population-pyramid plots separately for overweight/obese and current/former smoker. Data were derived from five National Health Interview Surveys carried out in Italy between 1983 and 2005. For each survey, data on age, gender, smoking status, height, and weight were extracted for subjects aged 20–99. Age and gender-specific prevalence rates of overweight/obese, and of current/former/never smoker were computed and applied to population estimates performed by the Italian national statistical institute (ISTAT). The resulting estimated numbers of underweight/normal weight/overweight/obese individuals and of former/current/never smokers were used to create the modified population pyramids. In conclusion, modified population pyramids may contribute to assessing the impact of risk factors on a population in absolute terms, to evaluating how these risk factors are distributed by age and gender, and to assessing how the age and gender distribution of these risk factors changes over time.

ESPOONERBAC_{{ERBAC}}: Enforcing Security Policies In Outsourced Environments

Data outsourcing is a growing business model offering services to individuals and enterprises for processing and storing a huge amount of data. It is not only economical but also promises higher availability, scalability, and more effective quality of service than in-house solutions. Despite all its benefits, data outsourcing raises serious security concerns for preserving data confidentiality. There are solutions for preserving confidentiality of data while supporting search on the data stored in outsourced environments. However, such solutions do not support access policies to regulate access to a particular subset of the stored data. For complex user management, large enterprises employ Role-Based Access Controls (RBAC) models for making access decisions based on the role in which a user is active in. However, RBAC models cannot be deployed in outsourced environments as they rely on trusted infrastructure in order to regulate access to the data. The deployment of RBAC models may reveal private information about sensitive data they aim to protect. In this paper, we aim at filling this gap by proposing \textbf{$\mathit{ESPOON_{ERBAC}}$} for enforcing RBAC policies in outsourced environments. $\mathit{ESPOON_{ERBAC}}$ enforces RBAC policies in an encrypted manner where a curious service provider may learn a very limited information about RBAC policies. We have implemented $\mathit{ESPOON_{ERBAC}}$ and provided its performance evaluation showing a limited overhead, thus confirming viability of our approach.Comment: The final version of this paper has been accepted for publication in Elsevier Computers & Security 2013. arXiv admin note: text overlap with arXiv:1306.482